01613nas a2200229   4500000000100000000000100001008004100002260001200043653002800055653002500083653002700108100002100135700002500156700002000181700002300201245010800224856008100332300000800413490001300421520093500434022001401369       9998                            d  c04/202310aCollaborative Filtering10aMatrix Factorization10aRecommendation Systems1 aJesús Bobadilla1 aJorge Dueñas-Lerín1 aFernando Ortega1 aAbraham Gutiérrez00aComprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems  uhttps://www.ijimai.org/journal/sites/default/files/2023-04/ip2023_04_008.pdf  a1-90 vIn Press3 aMatrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided.  a1989-1660